Abstract
Gasification is a thermochemical conversion process that has gained increasing attention in both academic and industrial research due to its potential for producing syngas from biomass. In a downdraft gasifier, this process breaks down into four distinct zones - drying, pyrolysis, oxidation, and reduction—each with its own complex physical and chemical mechanisms. Although promising, optimizing the gasification process remains difficult because of the system’s intricate nature and the variability in biomass feedstock properties. Accurately predicting the composition of syngas and the temperature profiles across the gasifier is crucial for improving efficiency and guiding the design of more effective gasification systems. This study aims to create a comprehensive mathematical model in MATLAB for a downdraft gasifier, incorporating the four zones – drying, pyrolysis, oxidation, and reduction – using a kinetic methodology. Utilizing rubber wood as the feedstock, the MATLAB model aims to simulate the gasification process under varying conditions, predict final syngas composition, and assist in optimizing critical parameters like equivalence ratio and feedstock moisture content. The model was validated using data from literature under similar conditions, achieving an average percentage error of 12.61% across seven production runs, where moisture content and equivalence ratio were varied independently. To explore the effects of these parameters, a sensitivity analysis is conducted by varying the moisture content between 0% to 30% while maintaining an equivalence ratio of 0.34. Further analysis was conducted by varying the equivalence ratio between 0.2 to 0.8 while maintaining the moisture content at approximately 15%. The results of this analysis suggest that lower moisture content below 15% and equivalence ratio below 0.35 are ideal for the converting rubber wood to syngas. However, the model’s predictive capability diminishes at higher equivalence ratios, indicating its current limitations in accurately forecasting gas composition under these conditions. These findings highlight the need for further refinement and development of the model to address the uncertainties and improve its accuracy at higher equivalence ratios.